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. Large-scale fire emission estimates may be influenced bythe spatial resolution of the model and input datasets used. Especially inareas with relatively heterogeneous land cover, a coarse model resolutionmight lead to substantial errors in estimates. We developed amodel using MODerate resolution Imaging Spectroradiometer (MODIS) satelliteobservations of burned area and vegetation characteristics to study theimpact of spatial resolution on modelled fire emission estimates. Weestimated fire emissions for sub-Saharan Africa at 500 m spatialresolution (native MODIS burned area) for the 2002–2017 period, using asimplified version of the Global Fire Emissions Database (GFED) modellingframework, and compared this to model runs at a range of coarser resolutions(0.050, 0.125, 0.250 ∘ ). We estimated fireemissions of 0.68 Pg C yr −1 at 500 m resolution and 0.82 Pg C yr −1 at 0.25 ∘ resolution; a difference of 24 %. At0.25 ∘ resolution, our model results were relatively similar toGFED4, which also runs at 0.25 ∘ resolution, whereas our 500 mestimates were substantially lower. We found that lower emissions at finerresolutions are mainly the result of reduced representation errors whencomparing modelled estimates of fuel load and consumption to fieldmeasurements, as part of the model calibration. Additional errors stem fromthe model simulation at coarse resolution and lead to an additional 0.02 Pg C yr −1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average-or majority- aggregated values are propagated in the coarse-resolutionsimulation and affect the model parameterization and the final result. Weidentified at least three error mechanisms responsible for the differencesin estimates between 500 m and 0.25 ∘ resolution simulations,besides those stemming from representation errors in the calibrationprocess, namely (1) biome misclassification leading to errors inparameterization, (2) errors due to the averaging of input data and theassociated reduction in variability, and (3) a temporal mechanism related tothe aggregation of burned area in particular. Even though these mechanismslargely neutralized each other and only modestly affect estimates at acontinental scale, they lead to substantial error at regional scales withdeviations of up to a factor 4 and may affect large-scale estimatesdifferently for other continents. These findings could prove valuable inimproving coarse-resolution models and suggest the need for increasedspatial resolution in global fire emission models.

Modelling biomass burning emissions and the effect of spatial resolution: a case study for Africa based on the Global Fire Emissions Database (GFED)